2. CONDUCTING URBAN SUSTAINABILITY SPATIAL
ANALYSIS
As a measure of showing and proving the usefulness of GIS spatial analysis in the process of ranking the sustainability in the
urban design, a process was conducted in a practical manner starting with selecting the test area, selecting some indicator
related specific factors which are cultural, regional and climate specific, selecting the layers of feature classes that relate to
those indicator factors, then by combining the results scenarios of assessments were conducted for the purpose, to finally
present a result that further assessments and activities shall be built upon.
2.1
Sample Energy Data
The sample energy data provided was related to municipal buildings, a total number of six buildings data provided annual
energy consumption in kWh and besides this those buildings which have used energy reduction plans, has provided also the
saved amount in kWh. This sample data is extremely small and insufficient, but on the other hand can give the bases for
calculating approximate values of energy consumption, which will be refined in a later stage using more data collected.
But what is important here is not only get the amount of energy consumption but also if environmental friendly methods are
being used in a building to reduce the energy, what type of activities are those, how much do they effect the energy
conservation on annual or periodic bases, and most importantly as a result to that how much CO2 is reduced in tones. As an
example of that one building was producing 21961993 KWh per annum and with a reduction baseline of 8 managed to
save 1756959 KWh which is equivalent to a measure of 878.5 tones saving more than 70 thousand dollars for the first year of
the reduction plan, it is off course anticipated that an investment has been made for following such a strategy but its sure
benefiting for the long run. 2.2
Spatial Statistical Analysis to Calculate Energy
The geographic places, geometries, and attributes for the buildings provided in sample data, were spatially searched and
those falling within the test area where categorized. The plan is to use only those falling in the test area, but the results showed
the results on table 2 where variance and standard deviation of the calculated factors need to be refined.
Table 2 Statistic of averaging energy consumption The next step performed was based on a decision towards using
the sample data which falls outside the test area to enrich the sample without actually changing the test area when performing
the other activities and analysis, this is valid as the buildings have similarities, uses the same sources of energy, but
variability will not harm anyway in such a case, perhaps variability will make the sample more reliable to represent the
data. This is presented by Table 3 herein where an improvement of the variance and standard deviation can clearly be seen.
Table 3 Larger Sample of Statistic for averaging energy For further narrowing the gap embedded within the sample data,
those values were dropped comprising higher deviations or residuals. Further, the analysis was continued until a satisfaction
of values was reached as follows in table 4:
12052.4732 Variance
109.783756 STD
Table 4 Now using areas of buildings and the number of floors
attributes in the building data with the assumption of averaging The height of each floor to 3 meters, the equation that contain
the factor value was used and the hypothetical energy consumption for all the buildings within the test area were
calculated as shown by Map 3 on figure 1
Figure 1 Energy Map of the Test Area volume
Energy Factor
squared deviations
Build 1
368950 2196200
59.525681 682.86236
8 Build
2 5462.54
5555270 1016.9756
2 867353.78
2 Build
3 9823.64
5395370 549.22312
1 214893.25
5 384236.
2 3291264
85.657316 4
360976.63 3
Variance STD
600.81331 volume
energy fac
tor Squared
deviation Residual
Build 1
7928 1,382,480
17 4
5818 -387
Build 2
4969 1048860
21 1
12766 -351
Build 3
5251 5292610
10 07
827695 445
Build 4
368950 21962000
59 1487
-502 Build
5 5462
5555270 10
16 844348
454 Build
6 9823
5395370 54
9 203520
-12 389487
38205250 98
315939 Variance
562 STD
XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australia
323
Thus the resulted values were off course directly proportional to the volumes of buildings, know when adding other factors to
some particular building types or buildings falling into a certain zones, such as those within industrial areas, or using some
attributes such as the construction date, further if remote sensing will provide from images or Lidar data some parameters
that can indicate the building material or the greenness of a building, as much as if a green roof exits then a further
refinement can be inserted to the spatial tables and further analysis can indicate the relations to the mentioned factors to
the energy consumption and subsequently the reduction of the emitted CO2 caused by power plants.
2.3
First values and Maps of reduced CO2
The sample data associated some relationships between reduced energy values in KWh and the equivalent carbon dioxide
associated to it in tones, thus once the reduced energy is calculated maps of reduced CO2 can be generated out of which.
Nonetheless, CO2 values are more sophisticated and can be related to many types of factors, such as factors of greenery
areas, and road surfaces besides many others, but herein those tow feature class layers were selected as a start to the
assessment, the primitive CO2 reduction map is illustrated in figure 2 uses five classes of energy reduction in tons of the test
area, but again linearity to the volume is initially kept and will be verified by the other factors in further analysis and
assessments.
Figure 2 CO2 Reduction Map These are not actual CO2 values, but values that effect CO2
produced by the power plant supplying the energy to the area, as
it‟s clear from the image above more emission is caused by concentrated dwelling areas, the fact which shall be further
proved when using greenery and transformational feature Classes. The histogram generated from this data set as
illustrated in figure 3 show high repetitions at the lower CO2 reduction values.
Figure 3 Data Histogram of CO2 Reduced values
3. RASTER ANALYSIS AND CO2 VALUES